Interpretation des predictions maisons
Author : VotreNom
Description : Rapport Shapash pour maisons
Project_Name : Analyse lightgbm_maison
Model used : LGBMRegressor
Library : lightgbm.sklearn
Library version : 4.6.0
Model parameters :
| Parameter key | Parameter value |
|---|---|
| boosting_type | gbdt |
| objective | None |
| num_leaves | 43 |
| max_depth | 9 |
| learning_rate | 0.1269191174033963 |
| n_estimators | 176 |
| subsample_for_bin | 200000 |
| min_split_gain | 0.0 |
| min_child_weight | 0.001 |
| min_child_samples | 10 |
| subsample | 1.0 |
| subsample_freq | 0 |
| colsample_bytree | 1.0 |
| reg_alpha | 0.0 |
| reg_lambda | 0.0 |
| random_state | None |
| Parameter key | Parameter value |
|---|---|
| n_jobs | None |
| importance_type | split |
| _Booster | |
| _evals_result | {} |
| _best_score | defaultdict( |
| _best_iteration | 0 |
| _other_params | {} |
| _objective | regression |
| class_weight | None |
| _class_weight | None |
| _class_map | None |
| _n_features | 55 |
| _n_features_in | 55 |
| _classes | None |
| _n_classes | -1 |
| fitted_ | True |
| Training dataset | Prediction dataset | |
|---|---|---|
| number of features | NaN | 55 |
| number of observations | NaN | 2,783 |
| missing values | NaN | 0 |
| % missing values | NaN | 0 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | 222 |
| std | 133 |
| min | 0 |
| 25% | 102 |
| 50% | 222 |
| 75% | 339 |
| max | 448 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | 0.0159 |
| std | 1.02 |
| min | -1.72 |
| 25% | -0.704 |
| 50% | -0.177 |
| 75% | 0.426 |
| max | 3.66 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | -0.0333 |
| std | 0.997 |
| min | -0.996 |
| 25% | -0.996 |
| 50% | -0.541 |
| 75% | 0.874 |
| max | 1.62 |
| Prediction dataset | |
|---|---|
| distinct values | 5 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 5 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | 0.0223 |
| std | 0.98 |
| min | -4.93 |
| 25% | -0.375 |
| 50% | 0.122 |
| 75% | 0.677 |
| max | 1.58 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 1 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 8 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 4 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 5 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 4 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 4 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | 0.00942 |
| std | 1.02 |
| min | -1.53 |
| 25% | -0.9 |
| 50% | -0.204 |
| 75% | 0.565 |
| max | 5.58 |
| Prediction dataset | |
|---|---|
| distinct values | 7 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 5 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 1 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 8 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 8 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | -0.0067 |
| std | 0.986 |
| min | -2 |
| 25% | -0.777 |
| 50% | -0.154 |
| 75% | 0.694 |
| max | 2.66 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | -0.0175 |
| std | 0.995 |
| min | -2.94 |
| 25% | -0.632 |
| 50% | 0.0483 |
| 75% | 0.61 |
| max | 2.1 |
| Prediction dataset | |
|---|---|
| distinct values | 4 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | -0.0127 |
| std | 0.986 |
| min | -2.38 |
| 25% | -0.357 |
| 50% | -0.357 |
| 75% | 0.149 |
| max | 7.24 |
| Prediction dataset | |
|---|---|
| distinct values | 8 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | -0.00689 |
| std | 0.989 |
| min | -0.407 |
| 25% | -0.396 |
| 50% | -0.357 |
| 75% | -0.197 |
| max | 3.61 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | -0.0115 |
| std | 0.978 |
| min | -1.27 |
| 25% | -0.791 |
| 50% | -0.131 |
| 75% | 0.55 |
| max | 6.25 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | -0.0081 |
| std | 0.989 |
| min | -0.404 |
| 25% | -0.396 |
| 50% | -0.366 |
| 75% | -0.221 |
| max | 3.63 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | -0.0296 |
| std | 0.941 |
| min | -1.33 |
| 25% | -0.826 |
| 50% | -0.188 |
| 75% | 0.58 |
| max | 5.48 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | 0.00293 |
| std | 1.02 |
| min | -0.608 |
| 25% | -0.503 |
| 50% | -0.387 |
| 75% | -0.134 |
| max | 4.12 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | -0.000241 |
| std | 1.05 |
| min | -0.656 |
| 25% | -0.542 |
| 50% | -0.341 |
| 75% | 0.0772 |
| max | 8.01 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | -0.00509 |
| std | 0.989 |
| min | -0.399 |
| 25% | -0.381 |
| 50% | -0.354 |
| 75% | -0.18 |
| max | 3.6 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | 0.0322 |
| std | 1.03 |
| min | -2.04 |
| 25% | -0.644 |
| 50% | -0.118 |
| 75% | 0.411 |
| max | 5.16 |
| Prediction dataset | |
|---|---|
| distinct values | 1 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 1 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 8 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | 0.00658 |
| std | 1.04 |
| min | -0.954 |
| 25% | -0.954 |
| 50% | 0.0667 |
| 75% | 0.388 |
| max | 4.2 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | -0.00842 |
| std | 0.984 |
| min | -0.436 |
| 25% | -0.404 |
| 50% | -0.364 |
| 75% | -0.185 |
| max | 3.83 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | -0.0106 |
| std | 1.01 |
| min | -6.72 |
| 25% | -0.729 |
| 50% | -0.145 |
| 75% | 0.605 |
| max | 3.38 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | -0.0184 |
| std | 1 |
| min | -1.64 |
| 25% | -0.619 |
| 50% | -0.31 |
| 75% | 0.229 |
| max | 7.86 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | -0.0261 |
| std | 0.899 |
| min | -0.81 |
| 25% | -0.353 |
| 50% | -0.233 |
| 75% | 0.0151 |
| max | 9.52 |
| Prediction dataset | |
|---|---|
| count | 2,783 |
| mean | 2,710 |
| std | 951 |
| min | 150 |
| 25% | 2,100 |
| 50% | 2,700 |
| 75% | 3,250 |
| max | 8,190 |
Note : the explainability graphs were generated using the test set only.
| True values | Prediction values | |
|---|---|---|
| count | 2,783 | 2,783 |
| mean | 2,710 | 2,700 |
| std | 951 | 762 |
| min | 150 | 355 |
| 25% | 2,100 | 2,220 |
| 50% | 2,700 | 2,710 |
| 75% | 3,250 | 3,180 |
| max | 8,190 | 6,340 |
MAE : 402
R2 : 0.661
MSE : 306,000
MAPE : 0.172
MdAE : 305
Explained Variance : 0.661